Biomarker-based Bayesian-adaptive trial design

Rationale

The goal of this project is to make all the necessary preparations for running and analyzing a clinical trial to decide on the optimal targeted treatment strategy for patients with diffuse large B-cell lymphoma (DLBCL). DLBCL is a very heterogeneous disease and treatment outcome for the existing therapies differs greatly between patients. With more becoming known about the various biological pathways involved in DLBCL, treatment of patients with DLBCL will likely become more successful if therapy can be matched to the patient’s tumor biology results. The results of the trial should guide medical doctors in their decision on the optimal therapy for a specific DLBCL patient taking into account the patient’s biological data.

Approach

A Bayesian-adaptive trial will be designed to identify the optimal targeted treatment strategy. Bayesian strategies in clinical trials allow for pre-planned alterations of the trial, such as the adaptation of the randomization probabilities and the adding and dropping of treatments, based on accumulating data. Due to this flexibility, Bayesian-adaptive trials are more suitable than standard randomized clinical trials for finding optimal targeted treatment strategies for diseases where patients with different characteristics may benefit from different treatments. These trials, however, require more careful planning and extensive simulations to assess the operating characteristics of the trial. In this project different choices for trial alterations will be considered and compared to find the optimal Bayesian-adaptive trial design to answer the question.